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Characteristic Wavelengths Selection of Soluble Solids Content of Pear Based on NIR Spectral and LS-SVM

文献类型： 外文期刊

作者： Fan Shu-xiang 1 ; Huang Wen-qian 2 ; Li Jiang-bo 2 ; Zhao Chun-jiang 1 ; Zhang Bao-hua 2 ;

作者机构： 1.Northwest Agr & Forestry Univ, Coll Mech & Elect Engn, Yangling 712100, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

关键词： NIR spectroscopy;Characteristic wavelengths;Least squares-support vector machine;Soluble solids content;Pear

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2014 年 34 卷 8 期

页码：

收录情况： SCI

摘要： To improve the precision and robustness of the NIR model of the soluble solid content (SSC) on pear. The total number of 160 pears was for the calibration (n=120) and prediction (n=40). Different spectral pretreatment methods, including standard normal variate (SNV) and multiplicative scatter correction (MSC) were used before further analysis. A combination of genetic algorithm (GA) and successive projections algorithm (SPA) was proposed to select most effective wavelengths after uninformative variable elimination (UVE) from original spectra, SNV pretreated spectra and MSC pretreated spectra respectively. The selected variables were used as the inputs of least squares-support vector machine (LS-SVM) model to build models for determining the SSC of pear. The results indicated that LS-SVM model built using SNVE-UVE-GA-SPA on 30 characteristic wavelengths selected from full-spectrum which had 3112 wavelengths achieved the optimal performance. The correlation coefficient (R-p) and root mean square error of prediction (RMSEP) for prediction sets were 0. 956, 0. 271 for SSC. The model is reliable and the predicted result is effective. The method can meet the requirement of quick measuring SSC of pear and might be important for the development of portable instruments and online monitoring.

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